Published Paper Details:

ABSTRACT The main objective of this paper is to find the best model to predict the value of the stock market. During the process of considering various techniques and variables that must be taken into account, it is found out that techniques like random forest, support vector machine were not exploited fully. In, this paper it is about to present and review a more feasible method to predict the stock movement with higher accuracy. The first thing that have been taken into account is the dataset of the stock market prices from previous year. The dataset was pre-processed and tuned up for real analysis. Hence, this paper will also focus on data preprocessing of the raw dataset. Secondly, after preprocessing the data will be reviewed to use the random forest, support vector machine on the dataset and the outcomes it generates. In addition, the proposed paper examines the use of the prediction system in real-world settings and issues associated with the accuracy of the overall values given. The paper also presents a machine-learning model to predict the longevity of stock in a competitive market. The successful prediction of the stock will be a great asset for the stock market institutions and will provide real-life solutions to the problems that stock investors face.